ICML 2024PastReinforcement learning
ICML 2024 Workshop: Foundations of Reinforcement Learning and Control -- Connections and Perspectives
FoRLaC
- Submission deadline
- May 30, 2024, 11:59 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (72)
Fetched from OpenReview (v2) on 2026-06-10.
$\alpha$-Fair Contextual Bandits
A Best-of-both-worlds Algorithm for Bandits with Delayed Feedback with Robustness to Excessive Delays
A Policy Optimization Approach to the Solution of Unregularized Mean Field Games
A Pontryagin Perspective on Reinforcement Learning
A safe exploration approach to constrained Markov decision processes
A Simple and Adaptive Learning Rate for FTRL in Online Learning with Minimax Regret of $\Theta(T^{2/3})$ and its Application to Best-of-Both-Worlds
A Variational Formulation of Reinforcement Learning in Infinite-Horizon Markov Decision Processes
Adaptive Experimental Design for Policy Learning: Contextual Best Arm Identification
Bandits with Abstention under Expert Advice
Bandits with Preference Feedback: A Stackelberg Game Perspective
Bridging Distributional and Risk-Sensitive Reinforcement Learning: Balancing Statistical, Computational, and Risk Considerations
Bridging Distributionally Robust Learning and Offline RL: An Approach to Mitigate Distribution Shift and Partial Data Coverage
Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals
Certifying robustness to adaptive data poisoning
Chained Information-Theoretic Bounds and Tight Regret Rate for Linear Bandit Problems
Combining Neural Networks and Symbolic Regression for Analytical Lyapunov Function Discovery
Compatible Gradient Approximations for Actor-Critic Algorithms
CPeSFA: Empowering SFs for Policy Learning and Transfer in Continuous Action Spaces
DARE: The Deep Adaptive Regulator for Control of Uncertain Continuous-Time Systems
DeePC-Hunt: Data-enabled Predictive Control Hyperparameter Tuning via Differentiable Optimization
Defending Against Unknown Corrupted Agents: Reinforcement Learning of Adversarially Robust Nash Equilibria
Distributional Monte-Carlo Planning with Thompson Sampling in Stochastic Environments
Essentially Sharp Estimates on the Entropy Regularization Error in Discounted Markov Decision Processes
Event-Based Federated Q-Learning
Exploring Integrality Grip for Mixed-integer Programming by MCTS Planning
Finite Sample Identification: From Frequency to Time Domain
Finite-time convergence to an $\epsilon$-efficient Nash equilibrium in potential games
Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution
Hierarchical Reinforcement Learning and Model Predictive Control for Strategic Motion Planning in Autonomous Racing
Hybrid Recurrent Models Support Emergent Descriptions for Hierarchical Planning and Control
Identifiable latent bandits: Combining observational data and exploration for personalized healthcare
Improved Algorithms for Contextual Dynamic Pricing
Learning HJB Viscosity Solutions with PINNs for Continuous-Time Reinforcement Learning
Learning Nash Equilibria in Zero-Sum Markov Games: A Single-Timescale Algorithm Under Weak Reachability
Learning to Explore with Lagrangians for Bandits under Unknown Constraints
Learning When to Trust the Expert for Guided Exploration in RL
Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy
Model Based Diffusion for Trajectory Optimization
Multiple-policy Evaluation via Density Estimation
NEORL: Efficient Exploration for Nonepisodic RL
Neural Dueling Bandits
Non-ergodicity in reinforcement learning: robustness via ergodicity transformations
Non-Linear $H_\infty$ Robustness Guarantees for Neural Network Policies
On PI Controllers for Updating Lagrange Multipliers in Constrained Optimization
On Robust Reinforcement Learning with Lipschitz-Bounded Policy Networks
Online Optimization of Closed-Loop Control Systems
Online Performance Optimization of Nonlinear Systems: A Gray-Box Approach
Optimality of Stationary Policies in Risk-averse Total-reward MDPs with EVaR
Optimistic Information Directed Sampling
Partial Structure Discovery is Sufficient for No-regret Learning in Causal Bandits
Pink Noise LQR: How does Colored Noise affect the Optimal Policy in RL?
Power Mean Estimation in Stochastic Monte-Carlo Tree Search
Preference Elicitation for Offline Reinforcement Learning
Randomized Confidence Bounds for Stochastic Partial Monitoring
Recommender System Design via Online Feedback Optimization
Recurrent Natural Policy Gradient for POMDPs
Reinforcement Learning of Adaptive Acquisition Policies for Inverse Problems
Reinforcement Learning with Lookahead Information
Reinforcement Learning with Quasi-Hyperbolic Discounting
Robust Best-of-Both-Worlds Gap Estimators Based on Importance-Weighted Sampling
Safe online nonstochastic control from data
Safe Reinforcement Learning with Contrastive Risk Prediction
SMX: Sequential Monte Carlo Planning for Expert Iteration
Sum-Max Submodular Bandits
The Minimax Regret of Sequential Probability Assignment, Contextual Shtarkov Sums, and Contextual Normalized Maximum Likelihood
The Value of Reward Lookahead in Reinforcement Learning
Tight Bounds for Online Convex Optimization with Adversarial Constraints
Towards Empowerment Gain through Causal Structure Learning in Model-Based RL
Truly No-Regret Learning in Constrained MDPs
Uniform Last-Iterate Guarantee for Bandits and Reinforcement Learning
Variance-Dependent Regret Bounds for Nonstationary Linear Bandits
When is Mean-Field Reinforcement Learning Tractable and Relevant?